It is not appropriate to use labeled activity data from an auxiliary dataset directly, due to different distributions between the auxiliary and the target datasets. Domain adaptation (DA) [1]} is a popular paradigm to bridge the distribution gap between two domains for knowledge transfer.
Thus, its key is to match the cross-domain distributions.
A fruitful line of work [2]}, [3]} on DA based HAR has achieved great success. We divide these work into two categories according to their different distribution matching schemes. The first category is rough matching which includes the domain-level matching methods [4]}, [5]}, the class-level matching methods [6]}, [7]} and both domain- and class-level matching methods [8]}. These works try to match distributions by learning domain-invariant representations, class-invariant representations, or invariant distributions in both domain and class. The other category is sample-level matching such as Optimal Transport for Domain Adaptation (OTDA) [9]} and Hypergraph Matching based Domain Adaptation [10]}, [11]}. The core of these works is to achieve pair-wise sample alignment for two domains.
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